Instructions to use MaziyarPanahi/calme-2.1-qwen2-7b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use MaziyarPanahi/calme-2.1-qwen2-7b with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="MaziyarPanahi/calme-2.1-qwen2-7b")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("MaziyarPanahi/calme-2.1-qwen2-7b")
model = AutoModelForMultimodalLM.from_pretrained("MaziyarPanahi/calme-2.1-qwen2-7b")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use MaziyarPanahi/calme-2.1-qwen2-7b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "MaziyarPanahi/calme-2.1-qwen2-7b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "MaziyarPanahi/calme-2.1-qwen2-7b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/MaziyarPanahi/calme-2.1-qwen2-7b

SGLang

How to use MaziyarPanahi/calme-2.1-qwen2-7b with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "MaziyarPanahi/calme-2.1-qwen2-7b" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "MaziyarPanahi/calme-2.1-qwen2-7b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "MaziyarPanahi/calme-2.1-qwen2-7b" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "MaziyarPanahi/calme-2.1-qwen2-7b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use MaziyarPanahi/calme-2.1-qwen2-7b with Docker Model Runner:
```
docker model run hf.co/MaziyarPanahi/calme-2.1-qwen2-7b
```

Update README.md

by MaziyarPanahi - opened Sep 19, 2024

base: refs/heads/main

←

from: refs/pr/8

Discussion Files changed

+12

-15

Files changed (1) hide show

README.md +12 -15

README.md CHANGED Viewed

@@ -131,9 +131,19 @@ This is a fine-tuned version of the `Qwen/Qwen2-7B` model. It aims to improve th
 All GGUF models are available here: [MaziyarPanahi/calme-2.1-qwen2-7b](https://huggingface.co/MaziyarPanahi/calme-2.1-qwen2-7b)
-# 🏆 [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
-coming soon!
 # Prompt Template
@@ -174,16 +184,3 @@ from transformers import AutoTokenizer, AutoModelForCausalLM
 tokenizer = AutoTokenizer.from_pretrained("MaziyarPanahi/calme-2.1-qwen2-7b")
 model = AutoModelForCausalLM.from_pretrained("MaziyarPanahi/calme-2.1-qwen2-7b")
 ```
-# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard)
-Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_MaziyarPanahi__calme-2.1-qwen2-7b)
-|      Metric       |Value|
-|-------------------|----:|
-|Avg.               |23.20|
-|IFEval (0-Shot)    |38.16|
-|BBH (3-Shot)       |31.01|
-|MATH Lvl 5 (4-Shot)|21.07|
-|GPQA (0-shot)      | 5.26|
-|MuSR (0-shot)      |13.80|
-|MMLU-PRO (5-shot)  |29.92|

 All GGUF models are available here: [MaziyarPanahi/calme-2.1-qwen2-7b](https://huggingface.co/MaziyarPanahi/calme-2.1-qwen2-7b)
+# 🏆 [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard)
+Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_MaziyarPanahi__calme-2.1-qwen2-7b)
+|      Metric       |Value|
+|-------------------|----:|
+|Avg.               |23.20|
+|IFEval (0-Shot)    |38.16|
+|BBH (3-Shot)       |31.01|
+|MATH Lvl 5 (4-Shot)|21.07|
+|GPQA (0-shot)      | 5.26|
+|MuSR (0-shot)      |13.80|
+|MMLU-PRO (5-shot)  |29.92|
 # Prompt Template
 tokenizer = AutoTokenizer.from_pretrained("MaziyarPanahi/calme-2.1-qwen2-7b")
 model = AutoModelForCausalLM.from_pretrained("MaziyarPanahi/calme-2.1-qwen2-7b")
 ```